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this will all be done within Excel. We will introduce some often used and important
inferential statistics techniques in this chapter.
6.2 Let the Statistical Technique Fit the Data
Consider the type of sample data we have seen thus far in Chaps. 1–5. In just about
every case, the data has contained a combination of quantitative and qualitative data
elements. For example, the data for teens visiting websites in Chap. 3 provided the
number of page views for each teen, and also described the circumstances related
to the page views—either new or old site. This was our first exposure to sophisti-
cated statistics and to cause and effect analysis—one variable causing an effect on
another. We can think of these categories, new and old, as experimental treatments ,
and the page views as a response variable . Thus, the treatment is the assumed
cause and the effect is the number of views. In an attempt to determine if the sam-
ple means of the two treatments were different or equal, we performed an analysis
called a paired t-Test . This test permitted us to consider complicated questions.
So when do we need this more sophisticated statistical analysis? Some of the
answers to this question can be summarized as follows:
1. When we want to make a precise mathematical statement about the data’s
capability to infer characteristics of the population.
2. When we want to determine how closely these data fit some assumed model of
3. When we need a higher level of analysis to further investigate the preliminary
findings of descriptive and exploratory analysis.
This chapter will focus on data that has both qualitative and quantitative com-
ponents, but we will also consider data that is strictly qualitative (categorical), as
you will soon see. By no means can we explore the exhaustive set of statistical
techniques available for these data types; there are thousands of techniques avail-
able and more are being developed as we speak. But, we will introduce some of the
most often used tools in statistical analysis. Finally, I repeat that it is important to
remember that the type of data we are analyzing will dictate the technique that we
can employ. The misapplication of a technique on a particular set of data is the most
common reason for dismissing or ignoring the results of an analysis; the analysis
just does not match the data.
2 —Chi-Square Test of Independence for Categorical Data
Let us begin with a powerful analytical tool applied to a frequently occurring type
of data—categorical variables. In this analysis, a test is conducted on sample data,
and the test attempts to determine if there is an association, or relationship, between
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